A Survey of Classification Techniques in the Area of Big Data

TL;DR

This survey reviews supervised classification techniques for big data, discussing their advantages and limitations, to facilitate better data organization and access in large, complex datasets.

Contribution

It provides a comprehensive overview of supervised classification methods in big data, highlighting their benefits and challenges, which was lacking in prior surveys.

Findings

Supervised techniques help organize big data effectively.

Limitations include computational complexity and data quality issues.

Advantages include improved data accessibility.

Abstract

Big Data concern large-volume, growing data sets that are complex and have multiple autonomous sources. Earlier technologies were not able to handle storage and processing of huge data thus Big Data concept comes into existence. This is a tedious job for users unstructured data. So, there should be some mechanism which classify unstructured data into organized form which helps user to easily access required data. Classification techniques over big transactional database provide required data to the users from large datasets more simple way. There are two main classification techniques, supervised and unsupervised. In this paper we focused on to study of different supervised classification techniques. Further this paper shows a advantages and limitations.